Research/Scholarly/Creative Works

Conference Contributions - Published

• Kostyleva O., Paramonov V., Shigarov A. and Vetrova V. (2022) Towards Comparison of Table Type Taxonomies. In 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022 - Proceedings: 1461-1465. http://dx.doi.org/10.23919/MIPRO55190.2022.9803520.

• Maliszewski K., Kolenderska S. and Vetrova V. (2022) Removing parasitic elements from Quantum Optical Coherence Tomography data with Convolutional Neural Networks. In ICML 2022, AI for science workshop.

• Maliszewski KA., Vetrova V., Li H., Kolenderski P. and Kolenderska SM. (2022) Dispersion-contrast imaging using machine learning. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11948 http://dx.doi.org/10.1117/12.2612671.

• Ding N., Vetrova V., Bryan K. and Delaux S. (2021) Deep Learning for Spatiotemporal Anomaly Forecasting: A Case Study of Marine Heatwaves. In ICML 2021 Workshop on Tackling Climate Change with Machine Learning.

• Paramonov V., Shigarov A. and Vetrova V. (2021) Rule Driven Spreadsheet Data Extraction from Statistical Tables: Case Study. In Communications in Computer and Information Science 1486 CCIS: 84-95. http://dx.doi.org/10.1007/978-3-030-88304-1_7.

• Paramonov V., Shigarov A. and Vetrova V. (2020) Table Header Correction Algorithm Based on Heuristics for Improving Spreadsheet Data Extraction. In Communications in Computer and Information Science 1283 CCIS: 147-158. http://dx.doi.org/10.1007/978-3-030-59506-7_13.

• Paramonov V., Shigarov A., Vetrova V. and Mikhailov A. (2020) Heuristic Algorithm for Recovering a Physical Structure of Spreadsheet Header. In Advances in Intelligent Systems and Computing 1050: 140-149. http://dx.doi.org/10.1007/978-3-030-30440-9_14.

• Coup S., Vetrova V., Frank E. and Rachael T. (2019) Domain specific transfer learning using image mixing and and stochastic image selection. In.

• Vetrova V., Coup S., Frank E. and Cree MJ. (2019) Hidden Features: Experiments with Feature Transfer for Fine-Grained Multi-Class and One-Class Image Categorization. In Proceedings - IVCNZ 2018: 6. http://dx.doi.org/10.1109/IVCNZ.2018.8634790.

• Vetrova V., Coup S., Frank E. and Cree M. (2018) Difference in details: transfer learning case study of “cryptic” plants and moths. In : 4.

• Mo J., Frank E. and Vetrova V. (2017) Large-scale automatic species identification. In Lecture Notes in Computer Science 10400: 301-312. Springer Verlag. http://dx.doi.org/10.1007/978-3-319-63004-5_24.

Journal Articles

• Maliszewski KA., Urbańska MA., Kolenderski P., Vetrova V. and Kolenderska SM. (2023) Extracting Group Velocity Dispersion values using quantum-mimic Optical Coherence Tomography and Machine Learning. Scientific Reports 13(1) http://dx.doi.org/10.1038/s41598-023-32592-7.

• Maliszewski KA., Kolenderski P., Vetrova V. and Kolenderska SM. (2022) Towards retrieving dispersion profiles using quantum-mimic optical coherence tomography and machine learning. Optics Express 30(25): 45624-45634. http://dx.doi.org/10.1364/OE.460079.

• Bardsley E. and Vetrova V. (2021) The bounded inverse Weibull distribution: An extreme value alternative for application to environmental maxima? Watershed Ecology and the Environment 3: 57-63. http://dx.doi.org/10.1016/j.wsee.2021.09.001.

• Bardsley E. and Vetrova V. (2019) Estimating evaporation for low wind speeds at an eddy correlation site: Potential for windbreak evaluation. Journal of Hydrology New Zealand 58(1): 57-63.

• Bardsley E., Vetrova V. and Dao NH. (2019) Line mesh distributions: an alternative approach for multivariate environmental extremes. Stochastic Environmental Research and Risk Assessment 33(2): 633-643. http://dx.doi.org/10.1007/s00477-018-1642-x.

• Mason NWH., Palmer DJ., Vetrova V., Brabyn L., Paul T., Willemse P. and Peltzer DA. (2017) Accentuating the positive while eliminating the negative of alien tree invasions: a multiple ecosystem services approach to prioritising control efforts. Biological Invasions 19(4): 1181-1195. http://dx.doi.org/10.1007/s10530-016-1307-y.

• Vetrova V. and Bardsley E. (2017) A simple nonparametric index of bivariate association for environmental data exploration. Environmental Modelling and Software 96: 283-290. http://dx.doi.org/10.1016/j.envsoft.2017.07.006.

• Vetrova V. and Bardsley WE. (2017) R Code for Data Simulation with Moment Matching. Open Water 4(1) 7: 72-74.

• Bardsley WE., Vetrova V. and Liu S. (2015) Toward creating simpler hydrological models: A LASSO subset selection approach. Environmental Modelling and Software 72: 33-43. http://dx.doi.org/10.1016/j.envsoft.2015.06.008.

• Shafie KA., Künnemeyer R., McGlone A., Talele S. and Vetrova V. (2015) An optimised six-wavelength model for predicting kiwifruit dry matter. Journal of Near Infrared Spectroscopy 23(2): 103-109. http://dx.doi.org/10.1255/jnirs.1151.

• Vetrova VV. and Bardsley WE. (2015) An association between Waitaki River winter headwater flows and the Interdecadal Pacific Oscillation (IPO). Journal of Hydrology New Zealand 54(2): 103-108.

• Vetrova VV. and Bardsley WE. (2012) Technical note: A significance test for data-sparse zones in scatter plots. Hydrology and Earth System Sciences 16(4): 1255-1257. http://dx.doi.org/10.5194/hess-16-1255-2012.

Reports

• Vetrova V. (2020) Validation report on machine learning methods for Automated identification of Halyomorpha halys (BMSB).Commissioned by Ministry for Primary Industries. 16.

Review and Refereeing

• ICANN2019 ( 2019 - 2023)
• Advances in Water Resources ( 2018 - 2023)
• Journal of Hydrology ( 2017 )
• Systematic Biology ( 2018 - 2023)

Future Research

• Spherical convolutional neural networks
• Time-evolving data science / artifical intelligence for advanced open environmental science
• Locally relevant / actionable seasonable climate forecasts